{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 第八周的作业densenet的实现部分如下：\n",
    "（下面是自己看论文的理解）\n",
    "1.densenet核心：以feed-forward方式连接每层与其他各层，优点：缓解梯度消失问题，增强特征扩展及特征重用，减少参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.从论文中读到，densenet的实现主要通过数个block组成，各个block用卷积池化层进行连接，称为转化层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.每个dense block 分为L层（层数可不同），第XL层的输入由第XL-1,XL-2,……X0层的输入组成，受到前面所有层输出的影响，层数越多越密集，因此称为稠密连接"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.block中每层的输出都经过一个组合函数（BN-RELU-CONV）处理，与前面的L-1所有层组合输入给下一层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5.其中，第X1层的输入有k0个特征图，经过组合函数处理均生成k个特征图，因此第X2的输入有k0+k个特征图，每增加一层，特征图就增加k个，第XL层的输入有k0+k*(L-1)个特征图，因此k称为growth rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.下面densenet实现参考论文table1中Imagenet的实现方法densenet-121"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def densenet(images, num_classes=200, is_training=False,\n",
    "             dropout_keep_prob=0.8,\n",
    "             scope='densenet'):   \n",
    "    growth = 24\n",
    "    compression_rate = 0.5\n",
    "\n",
    "    def reduce_dim(input_feature):\n",
    "        return int(int(input_feature.shape[-1]) * compression_rate)\n",
    "\n",
    "    end_points = {}\n",
    "\n",
    "    with tf.variable_scope(scope, 'DenseNet', [images, num_classes]):\n",
    "        with slim.arg_scope(bn_drp_scope(is_training=is_training,\n",
    "                                         keep_prob=dropout_keep_prob)) as ssc:        \n",
    "            #convolution 224x224x3 输入图片大小\n",
    "            end_point = \"convolution_7x7\"\n",
    "            logits = slim.conv2d(images, 2*growth, [7,7],stride = 2,padding = \"same\", scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "            #pooling 112x112x2g \n",
    "            end_point = \"max_pool_3x3\"\n",
    "            logits = slim.max_pool2d(logits,[3,3],stride = 2,padding = \"same\",scope = end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #block1 56x56x2g\n",
    "            end_point = \"block1\"\n",
    "            logits = block(logits, 6, growth,scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #transition layer1 56x56x(2g+g*6)=56x56x8g\n",
    "            end_point = \"tran_conv1\"\n",
    "            logits = bn_act_conv_drp(logits, reduce_dim(logits), [1,1],scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "            #56x56x4g\n",
    "            end_point = \"tran_pooling1\"\n",
    "            logits = slim.avg_pool2d(logits,[2,2],stride = 2,scope = end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #block2 28x28x4g\n",
    "            end_point = \"block2\"\n",
    "            logits = block(logits, 12, growth,scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #transition layer2 28x28x(4g+24*g)=28x28x28g\n",
    "            end_point = \"tran_conv2\"\n",
    "            logits = bn_act_conv_drp(logits, reduce_dim(logits), [1,1],scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "            #28x28x14g\n",
    "            end_point = \"tran_pooling2\"\n",
    "            logits = slim.avg_pool2d(logits,[2,2],stride = 2,scope = end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #block3 14x14x14g\n",
    "            end_point = \"block3\"\n",
    "            logits = block(logits, 24, growth,scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #transition layer3 14x14x(14g+24*g)=14x14x38g\n",
    "            end_point = \"tran_conv3\"\n",
    "            logits = bn_act_conv_drp(logits, reduce_dim(logits), [1,1],scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "            #14x14x19g\n",
    "            end_point = \"tran_pooling3\"\n",
    "            logits = slim.avg_pool2d(logits,[2,2],stride = 2,scope = end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #block4 7x7x19g\n",
    "            end_point = \"block4\"\n",
    "            logits = block(logits, 16, growth,scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "            #classification layer 7x7x(19g+16*g)=7x7x35g\n",
    "            #global average pooling\n",
    "            end_point = \"class_conv\"\n",
    "            logits = slim.avg_pool2d(logits, [7,7],scope=end_point)\n",
    "            end_points[end_point] = logits\n",
    "            #1x1x35g\n",
    "            end_point = \"class_fully_connected\"\n",
    "            logits = slim.conv2d(logits,num_classes,[1,1],activation_fn = tf.nn.softmax,scope = end_point)\n",
    "            end_points[end_point] = logits\n",
    "\n",
    "    return logits, end_points"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "采用上述densenet结构插入slim框架中训练，参数如下：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练1000步后，对验证集进行验证，参数及结果如下："
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
